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<p>Intersection holes in airplane structures require multi-pass boring operations combining rough and finish machining stages. Most current practices use fixed, conservative parameters that prioritize precision over efficiency, while empirical approaches cause quality inconsistencies. Inter-pass correlations from cumulative material removal remain underexplored. This paper proposes an accelerated rolling optimization framework that simultaneously improves machining efficiency and precision. The approach optimizes material removal rate for rough boring and surface roughness for finish boring by integrating a hybrid predictive model that fuses process parameters with sensor data, an enhanced multi-objective particle swarm optimization algorithm with powerball functions for faster convergence, and a rolling optimization mechanism that adaptively updates parameters between passes. Experimental validation demonstrates significant improvements: boring passes reduced from 13 to 8 while average surface roughness improved from <i>R</i><sub><i>a</i></sub> 1.682 to 1.079 µm.</p>

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An accelerated rolling optimization strategy for multi-pass boring operations with static-dynamic data fusion

  • 期刊论文

  • 发表日期：

    2026年5月25日

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  • Feng Hua,
  • Longjianjie Zhang,
  • Zhanpeng Yang,
  • Ruijuan Chen,
  • Cheng Cheng,
  • Ye Yuan,
  • Han Ding

摘要

Intersection holes in airplane structures require multi-pass boring operations combining rough and finish machining stages. Most current practices use fixed, conservative parameters that prioritize precision over efficiency, while empirical approaches cause quality inconsistencies. Inter-pass correlations from cumulative material removal remain underexplored. This paper proposes an accelerated rolling optimization framework that simultaneously improves machining efficiency and precision. The approach optimizes material removal rate for rough boring and surface roughness for finish boring by integrating a hybrid predictive model that fuses process parameters with sensor data, an enhanced multi-objective particle swarm optimization algorithm with powerball functions for faster convergence, and a rolling optimization mechanism that adaptively updates parameters between passes. Experimental validation demonstrates significant improvements: boring passes reduced from 13 to 8 while average surface roughness improved from Ra 1.682 to 1.079 µm.